Overview

Dataset statistics

Number of variables22
Number of observations1542
Missing cells12768
Missing cells (%)37.6%
Duplicate rows7
Duplicate rows (%)0.5%
Total size in memory277.1 KiB
Average record size in memory184.0 B

Variable types

Text2
Unsupported1
Categorical5
Numeric14

Alerts

Division has constant value ""Constant
Qualifier has constant value ""Constant
Dataset has 7 (0.5%) duplicate rowsDuplicates
Back Squat (lbs) is highly overall correlated with Clean and Jerk (lbs) and 3 other fieldsHigh correlation
Clean and Jerk (lbs) is highly overall correlated with Back Squat (lbs) and 5 other fieldsHigh correlation
Deadlift (lbs) is highly overall correlated with Back Squat (lbs) and 3 other fieldsHigh correlation
Fight Gone Bad is highly overall correlated with Filthy 50 (s) and 3 other fieldsHigh correlation
Filthy 50 (s) is highly overall correlated with Fight Gone Bad and 1 other fieldsHigh correlation
Fran (s) is highly overall correlated with Clean and Jerk (lbs) and 3 other fieldsHigh correlation
Games_Level is highly overall correlated with L1 Benchmark (s) and 1 other fieldsHigh correlation
Grace (s) is highly overall correlated with Clean and Jerk (lbs) and 4 other fieldsHigh correlation
Helen (s) is highly overall correlated with Fight Gone Bad and 1 other fieldsHigh correlation
L1 Benchmark (s) is highly overall correlated with Back Squat (lbs) and 12 other fieldsHigh correlation
Max Pull-ups is highly overall correlated with Fran (s) and 1 other fieldsHigh correlation
Rank is highly overall correlated with L1 Benchmark (s)High correlation
Region is highly overall correlated with Games_Level and 1 other fieldsHigh correlation
Run 5k (s) is highly overall correlated with L1 Benchmark (s)High correlation
Snatch (lbs) is highly overall correlated with Back Squat (lbs) and 3 other fieldsHigh correlation
Affiliate has 212 (13.7%) missing valuesMissing
Country has 1542 (100.0%) missing valuesMissing
Back Squat (lbs) has 91 (5.9%) missing valuesMissing
Clean and Jerk (lbs) has 79 (5.1%) missing valuesMissing
Deadlift (lbs) has 104 (6.7%) missing valuesMissing
Snatch (lbs) has 85 (5.5%) missing valuesMissing
Fight Gone Bad has 1184 (76.8%) missing valuesMissing
Max Pull-ups has 772 (50.1%) missing valuesMissing
Chad1000x (s) has 1519 (98.5%) missing valuesMissing
L1 Benchmark (s) has 1537 (99.7%) missing valuesMissing
Filthy 50 (s) has 1325 (85.9%) missing valuesMissing
Fran (s) has 445 (28.9%) missing valuesMissing
Grace (s) has 742 (48.1%) missing valuesMissing
Helen (s) has 1066 (69.1%) missing valuesMissing
Run 5k (s) has 944 (61.2%) missing valuesMissing
Sprint 400m (s) has 1121 (72.7%) missing valuesMissing
L1 Benchmark (s) is uniformly distributedUniform
Country is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-18 02:57:27.851981
Analysis finished2024-02-18 02:57:46.783261
Duration18.93 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct1534
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:46.933183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length13.643969
Min length8

Characters and Unicode

Total characters21039
Distinct characters75
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1526 ?
Unique (%)99.0%

Sample

1st rowMichael van Tonder
2nd rowJustin Swart
3rd rowSchalk Burger
4th rowJoubert Boshoff
5th rowDavid Segun
ValueCountFrequency (%)
tyler 26
 
0.8%
michael 22
 
0.7%
ryan 21
 
0.7%
justin 21
 
0.7%
thomas 20
 
0.6%
andrew 17
 
0.5%
jacob 17
 
0.5%
alex 17
 
0.5%
nicholas 16
 
0.5%
kyle 16
 
0.5%
Other values (2006) 3013
94.0%
2024-02-17T21:57:47.494133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1927
 
9.2%
e 1871
 
8.9%
1664
 
7.9%
n 1504
 
7.1%
r 1379
 
6.6%
o 1319
 
6.3%
i 1275
 
6.1%
l 1017
 
4.8%
s 843
 
4.0%
t 729
 
3.5%
Other values (65) 7511
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16077
76.4%
Uppercase Letter 3257
 
15.5%
Space Separator 1664
 
7.9%
Dash Punctuation 25
 
0.1%
Other Punctuation 16
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1927
12.0%
e 1871
11.6%
n 1504
9.4%
r 1379
 
8.6%
o 1319
 
8.2%
i 1275
 
7.9%
l 1017
 
6.3%
s 843
 
5.2%
t 729
 
4.5%
h 539
 
3.4%
Other values (34) 3674
22.9%
Uppercase Letter
ValueCountFrequency (%)
M 304
 
9.3%
J 277
 
8.5%
C 256
 
7.9%
S 224
 
6.9%
D 215
 
6.6%
A 205
 
6.3%
B 196
 
6.0%
T 175
 
5.4%
R 174
 
5.3%
L 155
 
4.8%
Other values (17) 1076
33.0%
Other Punctuation
ValueCountFrequency (%)
' 11
68.8%
. 5
31.2%
Space Separator
ValueCountFrequency (%)
1664
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19334
91.9%
Common 1705
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1927
 
10.0%
e 1871
 
9.7%
n 1504
 
7.8%
r 1379
 
7.1%
o 1319
 
6.8%
i 1275
 
6.6%
l 1017
 
5.3%
s 843
 
4.4%
t 729
 
3.8%
h 539
 
2.8%
Other values (61) 6931
35.8%
Common
ValueCountFrequency (%)
1664
97.6%
- 25
 
1.5%
' 11
 
0.6%
. 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20990
99.8%
None 49
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1927
 
9.2%
e 1871
 
8.9%
1664
 
7.9%
n 1504
 
7.2%
r 1379
 
6.6%
o 1319
 
6.3%
i 1275
 
6.1%
l 1017
 
4.8%
s 843
 
4.0%
t 729
 
3.5%
Other values (45) 7462
35.6%
None
ValueCountFrequency (%)
é 7
14.3%
í 7
14.3%
ó 6
12.2%
ö 5
10.2%
á 5
10.2%
ø 3
 
6.1%
č 2
 
4.1%
ñ 2
 
4.1%
ś 1
 
2.0%
Í 1
 
2.0%
Other values (10) 10
20.4%

Affiliate
Text

MISSING 

Distinct1127
Distinct (%)84.7%
Missing212
Missing (%)13.7%
Memory size24.1 KiB
2024-02-17T21:57:47.746470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length37
Median length30
Mean length17.157895
Min length11

Characters and Unicode

Total characters22820
Distinct characters86
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique959 ?
Unique (%)72.1%

Sample

1st rowCape CrossFit
2nd rowCrossFit Juggernaut
3rd rowCrossFit Eikestad
4th rowBen Lomond CrossFit
5th rowCrossFit Wanderlust
ValueCountFrequency (%)
crossfit 1330
42.0%
city 24
 
0.8%
west 15
 
0.5%
the 13
 
0.4%
north 11
 
0.3%
iron 10
 
0.3%
east 9
 
0.3%
valley 8
 
0.3%
fitness 7
 
0.2%
park 7
 
0.2%
Other values (1304) 1734
54.7%
2024-02-17T21:57:48.120265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 3059
13.4%
o 2003
 
8.8%
r 2002
 
8.8%
i 1947
 
8.5%
t 1925
 
8.4%
1838
 
8.1%
C 1544
 
6.8%
F 1441
 
6.3%
e 1027
 
4.5%
a 817
 
3.6%
Other values (76) 5217
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16005
70.1%
Uppercase Letter 4677
 
20.5%
Space Separator 1838
 
8.1%
Decimal Number 266
 
1.2%
Other Punctuation 25
 
0.1%
Dash Punctuation 6
 
< 0.1%
Format 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 3059
19.1%
o 2003
12.5%
r 2002
12.5%
i 1947
12.2%
t 1925
12.0%
e 1027
 
6.4%
a 817
 
5.1%
n 608
 
3.8%
l 475
 
3.0%
u 313
 
2.0%
Other values (30) 1829
11.4%
Uppercase Letter
ValueCountFrequency (%)
C 1544
33.0%
F 1441
30.8%
S 149
 
3.2%
B 144
 
3.1%
T 125
 
2.7%
M 113
 
2.4%
R 107
 
2.3%
A 105
 
2.2%
P 96
 
2.1%
L 95
 
2.0%
Other values (19) 758
16.2%
Decimal Number
ValueCountFrequency (%)
0 47
17.7%
1 43
16.2%
2 42
15.8%
5 24
9.0%
4 22
8.3%
3 22
8.3%
7 21
7.9%
9 18
 
6.8%
6 15
 
5.6%
8 12
 
4.5%
Other Punctuation
ValueCountFrequency (%)
' 15
60.0%
. 10
40.0%
Space Separator
ValueCountFrequency (%)
1838
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Format
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20682
90.6%
Common 2138
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 3059
14.8%
o 2003
9.7%
r 2002
9.7%
i 1947
9.4%
t 1925
9.3%
C 1544
 
7.5%
F 1441
 
7.0%
e 1027
 
5.0%
a 817
 
4.0%
n 608
 
2.9%
Other values (59) 4309
20.8%
Common
ValueCountFrequency (%)
1838
86.0%
0 47
 
2.2%
1 43
 
2.0%
2 42
 
2.0%
5 24
 
1.1%
4 22
 
1.0%
3 22
 
1.0%
7 21
 
1.0%
9 18
 
0.8%
6 15
 
0.7%
Other values (7) 46
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22794
99.9%
None 25
 
0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 3059
13.4%
o 2003
 
8.8%
r 2002
 
8.8%
i 1947
 
8.5%
t 1925
 
8.4%
1838
 
8.1%
C 1544
 
6.8%
F 1441
 
6.3%
e 1027
 
4.5%
a 817
 
3.6%
Other values (58) 5191
22.8%
None
ValueCountFrequency (%)
ç 3
12.0%
ö 3
12.0%
é 2
 
8.0%
ø 2
 
8.0%
ü 2
 
8.0%
è 2
 
8.0%
õ 1
 
4.0%
å 1
 
4.0%
Č 1
 
4.0%
ň 1
 
4.0%
Other values (7) 7
28.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

Country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1542
Missing (%)100.0%
Memory size24.1 KiB

Region
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
North America East
536 
North America West
395 
Europe
367 
Oceania
104 
South America
58 
Other values (2)
82 

Length

Max length18
Median length18
Mean length13.514916
Min length4

Characters and Unicode

Total characters20840
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAfrica
3rd rowAfrica
4th rowAfrica
5th rowAfrica

Common Values

ValueCountFrequency (%)
North America East 536
34.8%
North America West 395
25.6%
Europe 367
23.8%
Oceania 104
 
6.7%
South America 58
 
3.8%
Asia 47
 
3.0%
Africa 35
 
2.3%

Length

2024-02-17T21:57:48.274387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:57:48.407199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
america 989
28.6%
north 931
26.9%
east 536
15.5%
west 395
 
11.4%
europe 367
 
10.6%
oceania 104
 
3.0%
south 58
 
1.7%
asia 47
 
1.4%
africa 35
 
1.0%

Most occurring characters

ValueCountFrequency (%)
r 2322
11.1%
t 1920
 
9.2%
1920
 
9.2%
e 1855
 
8.9%
a 1815
 
8.7%
o 1356
 
6.5%
i 1175
 
5.6%
c 1128
 
5.4%
A 1071
 
5.1%
h 989
 
4.7%
Other values (11) 5289
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15458
74.2%
Uppercase Letter 3462
 
16.6%
Space Separator 1920
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2322
15.0%
t 1920
12.4%
e 1855
12.0%
a 1815
11.7%
o 1356
8.8%
i 1175
7.6%
c 1128
7.3%
h 989
6.4%
m 989
6.4%
s 978
6.3%
Other values (4) 931
6.0%
Uppercase Letter
ValueCountFrequency (%)
A 1071
30.9%
N 931
26.9%
E 903
26.1%
W 395
 
11.4%
O 104
 
3.0%
S 58
 
1.7%
Space Separator
ValueCountFrequency (%)
1920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18920
90.8%
Common 1920
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2322
12.3%
t 1920
10.1%
e 1855
9.8%
a 1815
9.6%
o 1356
 
7.2%
i 1175
 
6.2%
c 1128
 
6.0%
A 1071
 
5.7%
h 989
 
5.2%
m 989
 
5.2%
Other values (10) 4300
22.7%
Common
ValueCountFrequency (%)
1920
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 2322
11.1%
t 1920
 
9.2%
1920
 
9.2%
e 1855
 
8.9%
a 1815
 
8.7%
o 1356
 
6.5%
i 1175
 
5.6%
c 1128
 
5.4%
A 1071
 
5.1%
h 989
 
4.7%
Other values (11) 5289
25.4%

Division
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
Men
1542 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4626
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMen
2nd rowMen
3rd rowMen
4th rowMen
5th rowMen

Common Values

ValueCountFrequency (%)
Men 1542
100.0%

Length

2024-02-17T21:57:48.526093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:57:48.610094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
men 1542
100.0%

Most occurring characters

ValueCountFrequency (%)
M 1542
33.3%
e 1542
33.3%
n 1542
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3084
66.7%
Uppercase Letter 1542
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1542
50.0%
n 1542
50.0%
Uppercase Letter
ValueCountFrequency (%)
M 1542
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4626
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1542
33.3%
e 1542
33.3%
n 1542
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 1542
33.3%
e 1542
33.3%
n 1542
33.3%

Rank
Real number (ℝ)

HIGH CORRELATION 

Distinct1069
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean842.15953
Minimum1
Maximum2306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:48.709548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile85.05
Q1290.25
median696.5
Q31318.75
95-th percentile2071.85
Maximum2306
Range2305
Interquartile range (IQR)1028.5

Descriptive statistics

Standard deviation627.40738
Coefficient of variation (CV)0.74499825
Kurtosis-0.73957699
Mean842.15953
Median Absolute Deviation (MAD)473.5
Skewness0.59786805
Sum1298610
Variance393640.02
MonotonicityNot monotonic
2024-02-17T21:57:48.831589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2132 20
 
1.3%
2306 15
 
1.0%
1458 9
 
0.6%
522 6
 
0.4%
77 5
 
0.3%
73 5
 
0.3%
174 4
 
0.3%
164 4
 
0.3%
1031 4
 
0.3%
197 4
 
0.3%
Other values (1059) 1466
95.1%
ValueCountFrequency (%)
1 1
0.1%
3 1
0.1%
9 1
0.1%
15 1
0.1%
30 1
0.1%
31 1
0.1%
35 1
0.1%
37 1
0.1%
38 1
0.1%
39 2
0.1%
ValueCountFrequency (%)
2306 15
1.0%
2303 1
 
0.1%
2301 1
 
0.1%
2296 1
 
0.1%
2294 1
 
0.1%
2288 1
 
0.1%
2268 1
 
0.1%
2245 1
 
0.1%
2240 1
 
0.1%
2239 1
 
0.1%

Games_Level
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
North America East
536 
North America West
395 
Europe
367 
Oceania
104 
South America
58 
Other values (2)
82 

Length

Max length18
Median length18
Mean length13.514916
Min length4

Characters and Unicode

Total characters20840
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAfrica
3rd rowAfrica
4th rowAfrica
5th rowAfrica

Common Values

ValueCountFrequency (%)
North America East 536
34.8%
North America West 395
25.6%
Europe 367
23.8%
Oceania 104
 
6.7%
South America 58
 
3.8%
Asia 47
 
3.0%
Africa 35
 
2.3%

Length

2024-02-17T21:57:48.949476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:57:49.057644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
america 989
28.6%
north 931
26.9%
east 536
15.5%
west 395
 
11.4%
europe 367
 
10.6%
oceania 104
 
3.0%
south 58
 
1.7%
asia 47
 
1.4%
africa 35
 
1.0%

Most occurring characters

ValueCountFrequency (%)
r 2322
11.1%
t 1920
 
9.2%
1920
 
9.2%
e 1855
 
8.9%
a 1815
 
8.7%
o 1356
 
6.5%
i 1175
 
5.6%
c 1128
 
5.4%
A 1071
 
5.1%
h 989
 
4.7%
Other values (11) 5289
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15458
74.2%
Uppercase Letter 3462
 
16.6%
Space Separator 1920
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2322
15.0%
t 1920
12.4%
e 1855
12.0%
a 1815
11.7%
o 1356
8.8%
i 1175
7.6%
c 1128
7.3%
h 989
6.4%
m 989
6.4%
s 978
6.3%
Other values (4) 931
6.0%
Uppercase Letter
ValueCountFrequency (%)
A 1071
30.9%
N 931
26.9%
E 903
26.1%
W 395
 
11.4%
O 104
 
3.0%
S 58
 
1.7%
Space Separator
ValueCountFrequency (%)
1920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18920
90.8%
Common 1920
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2322
12.3%
t 1920
10.1%
e 1855
9.8%
a 1815
9.6%
o 1356
 
7.2%
i 1175
 
6.2%
c 1128
 
6.0%
A 1071
 
5.7%
h 989
 
5.2%
m 989
 
5.2%
Other values (10) 4300
22.7%
Common
ValueCountFrequency (%)
1920
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 2322
11.1%
t 1920
 
9.2%
1920
 
9.2%
e 1855
 
8.9%
a 1815
 
8.7%
o 1356
 
6.5%
i 1175
 
5.6%
c 1128
 
5.4%
A 1071
 
5.1%
h 989
 
4.7%
Other values (11) 5289
25.4%

Qualifier
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
quarterfinals
1542 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters20046
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquarterfinals
2nd rowquarterfinals
3rd rowquarterfinals
4th rowquarterfinals
5th rowquarterfinals

Common Values

ValueCountFrequency (%)
quarterfinals 1542
100.0%

Length

2024-02-17T21:57:49.179114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:57:49.267025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
quarterfinals 1542
100.0%

Most occurring characters

ValueCountFrequency (%)
a 3084
15.4%
r 3084
15.4%
q 1542
7.7%
u 1542
7.7%
t 1542
7.7%
e 1542
7.7%
f 1542
7.7%
i 1542
7.7%
n 1542
7.7%
l 1542
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20046
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3084
15.4%
r 3084
15.4%
q 1542
7.7%
u 1542
7.7%
t 1542
7.7%
e 1542
7.7%
f 1542
7.7%
i 1542
7.7%
n 1542
7.7%
l 1542
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 20046
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3084
15.4%
r 3084
15.4%
q 1542
7.7%
u 1542
7.7%
t 1542
7.7%
e 1542
7.7%
f 1542
7.7%
i 1542
7.7%
n 1542
7.7%
l 1542
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3084
15.4%
r 3084
15.4%
q 1542
7.7%
u 1542
7.7%
t 1542
7.7%
e 1542
7.7%
f 1542
7.7%
i 1542
7.7%
n 1542
7.7%
l 1542
7.7%

Back Squat (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct165
Distinct (%)11.4%
Missing91
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean397.03304
Minimum22.0462
Maximum551.155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:49.374714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum22.0462
5-th percentile315
Q1365
median396.8316
Q3430
95-th percentile485.0082
Maximum551.155
Range529.1088
Interquartile range (IQR)65

Descriptive statistics

Standard deviation53.951495
Coefficient of variation (CV)0.13588666
Kurtosis4.2292649
Mean397.03304
Median Absolute Deviation (MAD)33.0693
Skewness-0.64456722
Sum576094.95
Variance2910.7638
MonotonicityNot monotonic
2024-02-17T21:57:49.526061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405 85
 
5.5%
385 61
 
4.0%
425 58
 
3.8%
396.8316 54
 
3.5%
374.7854 53
 
3.4%
365 50
 
3.2%
375 48
 
3.1%
400 43
 
2.8%
440.924 37
 
2.4%
355 35
 
2.3%
Other values (155) 927
60.1%
(Missing) 91
 
5.9%
ValueCountFrequency (%)
22.0462 1
0.1%
35.27392 1
0.1%
69 1
0.1%
140 1
0.1%
175 2
0.1%
176.3696 1
0.1%
231.4851 1
0.1%
235 1
0.1%
240.30358 1
0.1%
250 1
0.1%
ValueCountFrequency (%)
551.155 1
 
0.1%
550 3
 
0.2%
545 2
 
0.1%
535 1
 
0.1%
530 1
 
0.1%
529.1088 1
 
0.1%
525 2
 
0.1%
520.29032 1
 
0.1%
518.0857 1
 
0.1%
515 8
0.5%

Clean and Jerk (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct156
Distinct (%)10.7%
Missing79
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean298.73282
Minimum26.45544
Maximum418.8778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:49.650956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.45544
5-th percentile242.5082
Q1275
median300
Q3321.43726
95-th percentile355
Maximum418.8778
Range392.42236
Interquartile range (IQR)46.43726

Descriptive statistics

Standard deviation37.046362
Coefficient of variation (CV)0.12401169
Kurtosis2.8954531
Mean298.73282
Median Absolute Deviation (MAD)24.4225
Skewness-0.61520797
Sum437046.11
Variance1372.4329
MonotonicityNot monotonic
2024-02-17T21:57:49.781653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
315 88
 
5.7%
275 71
 
4.6%
305 69
 
4.5%
300 62
 
4.0%
285 61
 
4.0%
325 55
 
3.6%
295 51
 
3.3%
286.6006 50
 
3.2%
308.6468 49
 
3.2%
335 48
 
3.1%
Other values (146) 859
55.7%
(Missing) 79
 
5.1%
ValueCountFrequency (%)
26.45544 1
0.1%
100 1
0.1%
135 1
0.1%
145 1
0.1%
154.3234 1
0.1%
165.3465 2
0.1%
176.3696 2
0.1%
187.3927 2
0.1%
195 1
0.1%
198.4158 1
0.1%
ValueCountFrequency (%)
418.8778 1
 
0.1%
400 1
 
0.1%
395 1
 
0.1%
393 1
 
0.1%
390.21774 1
 
0.1%
390 1
 
0.1%
385.8085 1
 
0.1%
385 4
0.3%
382 2
0.1%
381.39926 1
 
0.1%

Deadlift (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct160
Distinct (%)11.1%
Missing104
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean467.41437
Minimum28
Maximum740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:49.909358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile385
Q1435
median465
Q3505
95-th percentile555
Maximum740
Range712
Interquartile range (IQR)70

Descriptive statistics

Standard deviation60.296259
Coefficient of variation (CV)0.12899958
Kurtosis6.4573034
Mean467.41437
Median Absolute Deviation (MAD)35.0991
Skewness-0.95136797
Sum672141.86
Variance3635.6389
MonotonicityNot monotonic
2024-02-17T21:57:50.040728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 70
 
4.5%
440.924 69
 
4.5%
455 64
 
4.2%
475 60
 
3.9%
405 53
 
3.4%
462.9702 49
 
3.2%
505 46
 
3.0%
425 44
 
2.9%
435 44
 
2.9%
485.0164 43
 
2.8%
Other values (150) 896
58.1%
(Missing) 104
 
6.7%
ValueCountFrequency (%)
28 1
0.1%
41.88778 1
0.1%
45 1
0.1%
170 1
0.1%
176 1
0.1%
220 1
0.1%
220.462 1
0.1%
230 1
0.1%
235.89434 1
0.1%
240 1
0.1%
ValueCountFrequency (%)
740 1
 
0.1%
665 1
 
0.1%
635 2
 
0.1%
625 3
 
0.2%
620 1
 
0.1%
617.2936 1
 
0.1%
615 2
 
0.1%
605 2
 
0.1%
601.86126 1
 
0.1%
600 9
0.6%

Snatch (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)9.1%
Missing85
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean235.49191
Minimum1
Maximum315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:50.169114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile185
Q1215
median235
Q3255.73592
95-th percentile285
Maximum315
Range314
Interquartile range (IQR)40.73592

Descriptive statistics

Standard deviation31.938009
Coefficient of variation (CV)0.13562253
Kurtosis3.7567324
Mean235.49191
Median Absolute Deviation (MAD)20
Skewness-0.82929448
Sum343111.72
Variance1020.0364
MonotonicityNot monotonic
2024-02-17T21:57:50.297142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225 101
 
6.5%
245 74
 
4.8%
235 66
 
4.3%
255 63
 
4.1%
220.462 62
 
4.0%
205 61
 
4.0%
242.5082 51
 
3.3%
275 51
 
3.3%
215 47
 
3.0%
265 46
 
3.0%
Other values (123) 835
54.2%
(Missing) 85
 
5.5%
ValueCountFrequency (%)
1 1
 
0.1%
19.84158 1
 
0.1%
75 1
 
0.1%
110 1
 
0.1%
110.231 1
 
0.1%
121.2541 1
 
0.1%
127.86796 1
 
0.1%
132.2772 3
0.2%
135 2
0.1%
140 2
0.1%
ValueCountFrequency (%)
315 4
 
0.3%
308.6468 2
 
0.1%
308 1
 
0.1%
305 1
 
0.1%
301 1
 
0.1%
300 10
0.6%
299.82832 1
 
0.1%
297.6237 4
 
0.3%
295 3
 
0.2%
293.21446 3
 
0.2%

Fight Gone Bad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct173
Distinct (%)48.3%
Missing1184
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean368.82402
Minimum38
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:50.420276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile275.7
Q1330
median367.5
Q3409
95-th percentile474.3
Maximum588
Range550
Interquartile range (IQR)79

Descriptive statistics

Standard deviation64.363592
Coefficient of variation (CV)0.1745103
Kurtosis4.1259766
Mean368.82402
Median Absolute Deviation (MAD)40.5
Skewness-0.7064222
Sum132039
Variance4142.672
MonotonicityNot monotonic
2024-02-17T21:57:50.542922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
319 6
 
0.4%
409 6
 
0.4%
351 6
 
0.4%
420 6
 
0.4%
400 6
 
0.4%
350 6
 
0.4%
335 5
 
0.3%
315 5
 
0.3%
355 5
 
0.3%
340 5
 
0.3%
Other values (163) 302
 
19.6%
(Missing) 1184
76.8%
ValueCountFrequency (%)
38 1
 
0.1%
50 1
 
0.1%
69 1
 
0.1%
214 1
 
0.1%
236 1
 
0.1%
243 1
 
0.1%
253 1
 
0.1%
265 4
0.3%
266 2
0.1%
267 1
 
0.1%
ValueCountFrequency (%)
588 1
0.1%
536 1
0.1%
520 1
0.1%
511 1
0.1%
508 2
0.1%
502 1
0.1%
497 1
0.1%
490 1
0.1%
489 1
0.1%
486 1
0.1%

Max Pull-ups
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)10.4%
Missing772
Missing (%)50.1%
Infinite0
Infinite (%)0.0%
Mean49.797403
Minimum1
Maximum420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:50.670032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q140
median50
Q360
95-th percentile75
Maximum420
Range419
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.58603
Coefficient of variation (CV)0.41339567
Kurtosis138.63764
Mean49.797403
Median Absolute Deviation (MAD)10
Skewness7.9873573
Sum38344
Variance423.78465
MonotonicityNot monotonic
2024-02-17T21:57:50.794477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 129
 
8.4%
40 55
 
3.6%
60 49
 
3.2%
55 36
 
2.3%
30 36
 
2.3%
45 28
 
1.8%
70 22
 
1.4%
65 20
 
1.3%
51 19
 
1.2%
42 18
 
1.2%
Other values (70) 358
23.2%
(Missing) 772
50.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 2
0.1%
6 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
12 1
 
0.1%
14 3
0.2%
15 1
 
0.1%
16 1
 
0.1%
17 3
0.2%
ValueCountFrequency (%)
420 1
 
0.1%
200 1
 
0.1%
94 1
 
0.1%
90 2
 
0.1%
88 1
 
0.1%
87 1
 
0.1%
85 1
 
0.1%
82 2
 
0.1%
81 2
 
0.1%
80 9
0.6%

Chad1000x (s)
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)100.0%
Missing1519
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean3708.1304
Minimum2068
Maximum4855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:50.894189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2068
5-th percentile2645.7
Q13416.5
median3712
Q34150
95-th percentile4750.3
Maximum4855
Range2787
Interquartile range (IQR)733.5

Descriptive statistics

Standard deviation693.38257
Coefficient of variation (CV)0.1869898
Kurtosis0.13647978
Mean3708.1304
Median Absolute Deviation (MAD)437
Skewness-0.43719726
Sum85287
Variance480779.39
MonotonicityNot monotonic
2024-02-17T21:57:50.993011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4020 1
 
0.1%
3600 1
 
0.1%
4519 1
 
0.1%
3552 1
 
0.1%
3570 1
 
0.1%
3727 1
 
0.1%
4103 1
 
0.1%
4855 1
 
0.1%
3360 1
 
0.1%
2989 1
 
0.1%
Other values (13) 13
 
0.8%
(Missing) 1519
98.5%
ValueCountFrequency (%)
2068 1
0.1%
2635 1
0.1%
2742 1
0.1%
2989 1
0.1%
3153 1
0.1%
3360 1
0.1%
3473 1
0.1%
3552 1
0.1%
3570 1
0.1%
3600 1
0.1%
ValueCountFrequency (%)
4855 1
0.1%
4776 1
0.1%
4519 1
0.1%
4500 1
0.1%
4301 1
0.1%
4151 1
0.1%
4149 1
0.1%
4103 1
0.1%
4020 1
0.1%
3727 1
0.1%

L1 Benchmark (s)
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct5
Distinct (%)100.0%
Missing1537
Missing (%)99.7%
Memory size24.1 KiB
252.0
172.0
245.0
243.0
191.0

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters25
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row252.0
2nd row172.0
3rd row245.0
4th row243.0
5th row191.0

Common Values

ValueCountFrequency (%)
252.0 1
 
0.1%
172.0 1
 
0.1%
245.0 1
 
0.1%
243.0 1
 
0.1%
191.0 1
 
0.1%
(Missing) 1537
99.7%

Length

2024-02-17T21:57:51.095317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T21:57:51.186795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
252.0 1
20.0%
172.0 1
20.0%
245.0 1
20.0%
243.0 1
20.0%
191.0 1
20.0%

Most occurring characters

ValueCountFrequency (%)
2 5
20.0%
. 5
20.0%
0 5
20.0%
1 3
12.0%
5 2
 
8.0%
4 2
 
8.0%
7 1
 
4.0%
3 1
 
4.0%
9 1
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
80.0%
Other Punctuation 5
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5
25.0%
0 5
25.0%
1 3
15.0%
5 2
 
10.0%
4 2
 
10.0%
7 1
 
5.0%
3 1
 
5.0%
9 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5
20.0%
. 5
20.0%
0 5
20.0%
1 3
12.0%
5 2
 
8.0%
4 2
 
8.0%
7 1
 
4.0%
3 1
 
4.0%
9 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5
20.0%
. 5
20.0%
0 5
20.0%
1 3
12.0%
5 2
 
8.0%
4 2
 
8.0%
7 1
 
4.0%
3 1
 
4.0%
9 1
 
4.0%

Filthy 50 (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct180
Distinct (%)82.9%
Missing1325
Missing (%)85.9%
Infinite0
Infinite (%)0.0%
Mean1301.6544
Minimum890
Maximum2325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:51.297048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum890
5-th percentile995.2
Q11140
median1282
Q31439
95-th percentile1717.4
Maximum2325
Range1435
Interquartile range (IQR)299

Descriptive statistics

Standard deviation224.93235
Coefficient of variation (CV)0.17280497
Kurtosis2.1753725
Mean1301.6544
Median Absolute Deviation (MAD)148
Skewness0.98295717
Sum282459
Variance50594.561
MonotonicityNot monotonic
2024-02-17T21:57:51.423783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1182 3
 
0.2%
1185 3
 
0.2%
1365 3
 
0.2%
1128 3
 
0.2%
1521 2
 
0.1%
1541 2
 
0.1%
1082 2
 
0.1%
1463 2
 
0.1%
1080 2
 
0.1%
1468 2
 
0.1%
Other values (170) 193
 
12.5%
(Missing) 1325
85.9%
ValueCountFrequency (%)
890 1
0.1%
899 1
0.1%
900 1
0.1%
923 1
0.1%
930 2
0.1%
942 1
0.1%
967 1
0.1%
977 1
0.1%
985 1
0.1%
992 1
0.1%
ValueCountFrequency (%)
2325 1
0.1%
2123 1
0.1%
2049 1
0.1%
1827 1
0.1%
1824 1
0.1%
1814 1
0.1%
1800 2
0.1%
1737 1
0.1%
1730 1
0.1%
1727 1
0.1%

Fran (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct178
Distinct (%)16.2%
Missing445
Missing (%)28.9%
Infinite0
Infinite (%)0.0%
Mean172.65178
Minimum97
Maximum2260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:51.552852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum97
5-th percentile125
Q1140
median159
Q3181
95-th percentile250.2
Maximum2260
Range2163
Interquartile range (IQR)41

Descriptive statistics

Standard deviation88.7565
Coefficient of variation (CV)0.51407811
Kurtosis310.94585
Mean172.65178
Median Absolute Deviation (MAD)20
Skewness14.937898
Sum189399
Variance7877.7162
MonotonicityNot monotonic
2024-02-17T21:57:51.674802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137 24
 
1.6%
146 24
 
1.6%
162 23
 
1.5%
140 22
 
1.4%
136 22
 
1.4%
152 21
 
1.4%
139 18
 
1.2%
135 18
 
1.2%
157 18
 
1.2%
165 17
 
1.1%
Other values (168) 890
57.7%
(Missing) 445
28.9%
ValueCountFrequency (%)
97 1
 
0.1%
112 2
 
0.1%
113 1
 
0.1%
114 4
 
0.3%
115 4
 
0.3%
116 1
 
0.1%
117 4
 
0.3%
118 10
0.6%
119 2
 
0.1%
120 5
0.3%
ValueCountFrequency (%)
2260 1
0.1%
1331 1
0.1%
900 1
0.1%
778 1
0.1%
502 1
0.1%
422 1
0.1%
410 1
0.1%
391 1
0.1%
380 1
0.1%
378 1
0.1%

Grace (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct171
Distinct (%)21.4%
Missing742
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean137.815
Minimum59
Maximum769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:51.791766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile80
Q1107
median124.5
Q3153.25
95-th percentile213.05
Maximum769
Range710
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation66.040972
Coefficient of variation (CV)0.47920018
Kurtosis39.786347
Mean137.815
Median Absolute Deviation (MAD)21.5
Skewness5.2676327
Sum110252
Variance4361.41
MonotonicityNot monotonic
2024-02-17T21:57:51.923805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 26
 
1.7%
115 17
 
1.1%
108 17
 
1.1%
100 16
 
1.0%
120 15
 
1.0%
90 15
 
1.0%
114 14
 
0.9%
140 14
 
0.9%
117 13
 
0.8%
180 13
 
0.8%
Other values (161) 640
41.5%
(Missing) 742
48.1%
ValueCountFrequency (%)
59 1
 
0.1%
60 1
 
0.1%
61 2
 
0.1%
66 1
 
0.1%
69 2
 
0.1%
70 4
0.3%
71 1
 
0.1%
72 5
0.3%
73 1
 
0.1%
75 3
0.2%
ValueCountFrequency (%)
769 1
0.1%
740 1
0.1%
729 1
0.1%
665 1
0.1%
600 2
0.1%
550 1
0.1%
512 1
0.1%
382 1
0.1%
360 1
0.1%
345 1
0.1%

Helen (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct198
Distinct (%)41.6%
Missing1066
Missing (%)69.1%
Infinite0
Infinite (%)0.0%
Mean516.44538
Minimum373
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:52.056905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum373
5-th percentile430
Q1468
median502
Q3549
95-th percentile638.25
Maximum1200
Range827
Interquartile range (IQR)81

Descriptive statistics

Standard deviation73.761373
Coefficient of variation (CV)0.14282512
Kurtosis16.975838
Mean516.44538
Median Absolute Deviation (MAD)39
Skewness2.5369559
Sum245828
Variance5440.7402
MonotonicityNot monotonic
2024-02-17T21:57:52.179124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 10
 
0.6%
541 9
 
0.6%
481 7
 
0.5%
468 7
 
0.5%
510 7
 
0.5%
494 7
 
0.5%
498 7
 
0.5%
585 6
 
0.4%
551 6
 
0.4%
476 6
 
0.4%
Other values (188) 404
 
26.2%
(Missing) 1066
69.1%
ValueCountFrequency (%)
373 1
0.1%
375 1
0.1%
380 1
0.1%
381 1
0.1%
394 1
0.1%
401 1
0.1%
402 1
0.1%
405 1
0.1%
415 2
0.1%
418 1
0.1%
ValueCountFrequency (%)
1200 1
0.1%
920 1
0.1%
802 1
0.1%
773 1
0.1%
747 1
0.1%
720 1
0.1%
687 1
0.1%
681 1
0.1%
677 1
0.1%
670 2
0.1%

Run 5k (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct296
Distinct (%)49.5%
Missing944
Missing (%)61.2%
Infinite0
Infinite (%)0.0%
Mean1269.7441
Minimum920
Maximum2700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:52.297689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum920
5-th percentile1063.85
Q11172.25
median1256
Q31340.75
95-th percentile1503.6
Maximum2700
Range1780
Interquartile range (IQR)168.5

Descriptive statistics

Standard deviation156.36679
Coefficient of variation (CV)0.12314827
Kurtosis12.661018
Mean1269.7441
Median Absolute Deviation (MAD)84
Skewness2.0103591
Sum759307
Variance24450.573
MonotonicityNot monotonic
2024-02-17T21:57:52.483686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1320 16
 
1.0%
1200 14
 
0.9%
1140 11
 
0.7%
1260 11
 
0.7%
1290 10
 
0.6%
1185 9
 
0.6%
1380 9
 
0.6%
1500 8
 
0.5%
1198 7
 
0.5%
1221 6
 
0.4%
Other values (286) 497
32.2%
(Missing) 944
61.2%
ValueCountFrequency (%)
920 1
0.1%
930 1
0.1%
947 1
0.1%
960 1
0.1%
977 1
0.1%
985 1
0.1%
988 1
0.1%
990 2
0.1%
991 1
0.1%
992 1
0.1%
ValueCountFrequency (%)
2700 1
0.1%
2002 1
0.1%
1831 1
0.1%
1812 1
0.1%
1799 1
0.1%
1780 1
0.1%
1740 1
0.1%
1735 1
0.1%
1720 1
0.1%
1714 1
0.1%

Sprint 400m (s)
Real number (ℝ)

MISSING 

Distinct56
Distinct (%)13.3%
Missing1121
Missing (%)72.7%
Infinite0
Infinite (%)0.0%
Mean65.337292
Minimum45
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-02-17T21:57:52.618680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile52
Q158
median62
Q369
95-th percentile88
Maximum234
Range189
Interquartile range (IQR)11

Descriptive statistics

Standard deviation15.71109
Coefficient of variation (CV)0.24046129
Kurtosis46.346788
Mean65.337292
Median Absolute Deviation (MAD)5
Skewness5.2822496
Sum27507
Variance246.83834
MonotonicityNot monotonic
2024-02-17T21:57:52.751098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 38
 
2.5%
58 28
 
1.8%
62 24
 
1.6%
61 21
 
1.4%
65 17
 
1.1%
54 17
 
1.1%
55 17
 
1.1%
59 17
 
1.1%
56 16
 
1.0%
66 16
 
1.0%
Other values (46) 210
 
13.6%
(Missing) 1121
72.7%
ValueCountFrequency (%)
45 2
 
0.1%
46 1
 
0.1%
48 1
 
0.1%
49 3
 
0.2%
50 7
0.5%
51 7
0.5%
52 11
0.7%
53 5
 
0.3%
54 17
1.1%
55 17
1.1%
ValueCountFrequency (%)
234 1
0.1%
205 1
0.1%
130 1
0.1%
120 1
0.1%
111 1
0.1%
110 1
0.1%
108 1
0.1%
105 1
0.1%
102 1
0.1%
100 1
0.1%

Interactions

2024-02-17T21:57:45.073843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:28.519407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.686471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.890358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:32.159513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.084131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.310606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.467426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.811045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.882289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.998379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:41.173467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.671594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.836481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.153754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:28.599863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.767194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.975417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:32.243634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.173466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.386702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.548584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.891393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.956015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.075817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:41.447494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.746986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.917454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.233174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:28.684945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.849592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.061627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:32.990418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.259034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.462319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.636681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.964388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.030413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.156371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:41.534341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.828604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.008534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.327177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:28.777571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.938706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.156175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.086617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.351901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.544894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.906571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.045911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.107447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.245317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:41.631897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.905471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.154935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.419716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:28.873257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.029544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.254601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.189493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.444680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.634908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.989676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.125027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.183215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.335006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:41.723819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.986924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.254713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.513949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:28.957929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.120432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.344881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.276962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.532511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.717257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.082612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.201910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.274547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.420476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:41.816139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.070029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.338307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.586803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.036563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.199862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.439526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.361504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.614147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.827868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.154751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.279665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.345399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.504929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:41.892550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.160107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.415587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.664456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.116402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.291182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.548657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.442201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.709917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.906433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.233904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.360946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.431601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.583298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.040348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.245988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.493476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.737917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.191534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.371975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.637810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.519693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.785036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.982304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.310057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.433911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.516279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.656275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.147619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.331137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.565043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.814084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.268747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.457963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.718777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.597402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.866735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.058561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.392877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.515772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.597199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.740164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.235004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.418926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.643650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.906053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.345763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.540562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.805505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.685599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:34.948614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.146398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.470265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.589208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.684660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.818184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.319548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.509181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.743390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.993425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.440580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.633211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.901152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.779836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.043534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.226088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.564666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.658229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.759542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.908440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.416273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.599588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.834612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:46.072293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.516290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.712868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:31.978438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.867177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.128032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.311862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.646214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.738542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.843040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:40.990289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.493246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.676664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:44.913398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:46.142417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:29.602409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:30.804898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:32.067977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:33.964880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:35.218509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:36.389167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:37.729889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:38.808554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:39.919305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:41.082653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:42.583938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:43.755196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T21:57:45.004460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-17T21:57:52.859249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Back Squat (lbs)Chad1000x (s)Clean and Jerk (lbs)Deadlift (lbs)Fight Gone BadFilthy 50 (s)Fran (s)Games_LevelGrace (s)Helen (s)L1 Benchmark (s)Max Pull-upsRankRegionRun 5k (s)Snatch (lbs)Sprint 400m (s)
Back Squat (lbs)1.0000.2170.8100.7230.372-0.182-0.4980.068-0.488-0.2241.0000.247-0.3430.068-0.0510.726-0.285
Chad1000x (s)0.2171.0000.2480.473-0.127-0.2570.2250.403-0.0440.3390.000-0.384-0.1780.403-0.1130.405-0.333
Clean and Jerk (lbs)0.8100.2481.0000.6830.464-0.178-0.5360.065-0.573-0.2691.0000.310-0.3790.065-0.0340.876-0.332
Deadlift (lbs)0.7230.4730.6831.0000.409-0.168-0.4180.037-0.473-0.2441.0000.230-0.2800.037-0.0770.597-0.305
Fight Gone Bad0.372-0.1270.4640.4091.000-0.582-0.4710.030-0.565-0.5041.0000.410-0.3130.030-0.1390.433-0.230
Filthy 50 (s)-0.182-0.257-0.178-0.168-0.5821.0000.3060.2720.3340.4661.000-0.2700.1450.2720.269-0.1680.031
Fran (s)-0.4980.225-0.536-0.418-0.4710.3061.0000.0270.5390.3881.000-0.5240.2950.0270.178-0.4890.313
Games_Level0.0680.4030.0650.0370.0300.2720.0271.000-0.069-0.0461.0000.071-0.2351.000-0.0180.113-0.096
Grace (s)-0.488-0.044-0.573-0.473-0.5650.3340.539-0.0691.0000.4421.000-0.3690.2530.0530.167-0.5490.317
Helen (s)-0.2240.339-0.269-0.244-0.5040.4660.388-0.0460.4421.0001.000-0.2230.2020.0780.389-0.2700.287
L1 Benchmark (s)1.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000-1.0000.9001.0000.500-0.200NaN
Max Pull-ups0.247-0.3840.3100.2300.410-0.270-0.5240.071-0.369-0.223-1.0001.000-0.3020.074-0.1640.298-0.291
Rank-0.343-0.178-0.379-0.280-0.3130.1450.295-0.2350.2530.2020.900-0.3021.0000.2560.048-0.3970.152
Region0.0680.4030.0650.0370.0300.2720.0271.0000.0530.0781.0000.0740.2561.000-0.0180.113-0.096
Run 5k (s)-0.051-0.113-0.034-0.077-0.1390.2690.178-0.0180.1670.3890.500-0.1640.048-0.0181.000-0.0120.448
Snatch (lbs)0.7260.4050.8760.5970.433-0.168-0.4890.113-0.549-0.270-0.2000.298-0.3970.113-0.0121.000-0.290
Sprint 400m (s)-0.285-0.333-0.332-0.305-0.2300.0310.313-0.0960.3170.287NaN-0.2910.152-0.0960.448-0.2901.000

Missing values

2024-02-17T21:57:46.293771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T21:57:46.562756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
525Michael van TonderCape CrossFitNaNAfricaMen3.0Africaquarterfinals407.85470319.66990436.51476266.75902NaNNaNNaNNaNNaNNaN120.0NaN1329.0NaN
526Justin SwartCrossFit JuggernautNaNAfricaMen9.0Africaquarterfinals429.90090341.71610462.97020257.94054420.036.0NaNNaNNaN126.0100.0437.01158.057.0
527Schalk BurgerNaNNaNAfricaMen15.0Africaquarterfinals385.80850330.69300485.01640264.55440383.053.0NaNNaNNaN152.0113.0NaNNaNNaN
528Joubert BoshoffCrossFit EikestadNaNAfricaMen45.0Africaquarterfinals375.00000285.00000475.00000245.00000400.071.0NaNNaN1421.0147.0144.0NaN1238.0NaN
529David SegunBen Lomond CrossFitNaNAfricaMen47.0Africaquarterfinals480.00000345.00000475.00000275.00000NaNNaNNaNNaNNaN139.0NaNNaNNaNNaN
530Jacques Van Der WaltCrossFit WanderlustNaNAfricaMen49.0Africaquarterfinals462.97020343.92072507.06260277.78212350.045.0NaNNaNNaN140.090.0NaNNaNNaN
531Retief HoffmannNaNNaNAfricaMen56.0Africaquarterfinals374.78540297.62370485.01640253.53130350.050.0NaNNaNNaN174.0110.0615.01480.065.0
532Tafadzwa MushanduCrossFit PalaceNaNAfricaMen69.0Africaquarterfinals485.00000335.00000625.00000265.00000NaNNaNNaNNaNNaN140.073.0NaNNaNNaN
533Jaafar DaneCrossFit BumblebeesNaNAfricaMen76.0Africaquarterfinals240.30358198.41580264.55440154.32340NaN2.0NaNNaNNaNNaN120.0NaNNaNNaN
534Cornel PieterseCrossFit IraNaNAfricaMen77.0Africaquarterfinals462.97020341.71610529.10880242.50820NaN20.0NaNNaNNaN250.0NaNNaNNaNNaN
AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
43840Edwin Del PezoCompa CrossFitNaNSouth AmericaMen324.0South AmericaquarterfinalsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN60.0
43841Henry Jordan VecchiMaddock CrossFitNaNSouth AmericaMen325.0South Americaquarterfinals374.78540308.64680456.35634242.50820NaN33.0NaNNaNNaNNaNNaNNaNNaNNaN
43842Kevin BarraezNaNNaNSouth AmericaMen333.0South Americaquarterfinals485.01640341.71610440.92400264.55440NaNNaNNaNNaNNaN168.0NaNNaNNaNNaN
43843David ScarpimCrossFit PassosNaNSouth AmericaMen344.0South Americaquarterfinals374.78540253.53130352.73920194.00656NaN30.0NaNNaNNaNNaNNaNNaN1740.0NaN
43844Franco DubocCrossFit UnboundedNaNSouth AmericaMen356.0South Americaquarterfinals365.00000NaNNaNNaNNaNNaNNaNNaNNaN178.0NaNNaNNaNNaN
43845Mateus JungCrossFit CampecheNaNSouth AmericaMen363.0South Americaquarterfinals401.24084275.57750462.97020224.87124320.0NaNNaNNaNNaN204.0150.0550.01285.0NaN
43846Agustin Ezequiel GuiñazuNaNNaNSouth AmericaMen364.0South Americaquarterfinals396.83160275.57750485.01640209.43890NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
43847Maykon DoboszNaNNaNSouth AmericaMen368.0South Americaquarterfinals440.92400330.69300440.92400242.50820NaNNaNNaNNaNNaNNaN143.0NaNNaNNaN
43848Lucas SilvaNaNNaNSouth AmericaMen394.0South Americaquarterfinals507.06260385.80850529.10880286.60060NaNNaNNaNNaNNaNNaNNaNNaN1141.0NaN
43849Rafael SolanoCrossFit YasNaNSouth AmericaMen403.0South Americaquarterfinals374.78540277.78212496.03950227.07586NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

AthleteAffiliateRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)# duplicates
0Caleb MichauCrossFit Spa CityNorth America WestMen1236.0North America Westquarterfinals385.0000305.0000435.000225.000330.037.0NaNNaNNaN148.0164.0541.01524.088.02
1Cj Van KampenCrossFit HuntsvilleNorth America EastMen1450.0North America Eastquarterfinals335.0000285.0000415.000230.000295.033.0NaNNaN1584.0192.0144.0532.01348.0NaN2
2Justin CobbCrossFit 270North America EastMen1946.0North America Eastquarterfinals320.0000245.0000355.000205.000265.0NaNNaNNaNNaN238.0160.0651.01545.086.02
3Kyle O'DonnellBitterroot CrossFitNorth America WestMen1227.0North America Westquarterfinals385.0000265.0000535.000205.000276.050.0NaNNaN1541.0118.0NaNNaNNaNNaN2
4Matt GreenfieldKinetic Grit CrossFitNorth America EastMen2015.0North America Eastquarterfinals465.0000285.0000455.000225.000279.023.0NaNNaN1522.0142.0119.0555.0990.058.02
5Thomas FolignoDeepRoots CrossFitNorth America EastMen1612.0North America Eastquarterfinals385.0000275.0000475.000200.000NaN24.0NaNNaNNaN207.0234.0523.01044.057.02
6Vincent DijouxCrossFit Mont RoquefeuilEuropeMen1651.0Europequarterfinals374.7854297.6237440.924220.462NaN30.0NaNNaN1468.0190.0178.0NaNNaNNaN2